Since Census microdata is predominantly categorical, it requires synthesis methods that can handle categorical data. CART, a non-parametric method developed by Breiman et al. [1]}, can handle mixed type (and missing) data, and can capture complex interactions and non-linear relationships. CART is a predictive technique that recursively partitions the predictor space, using binary splits, into relatively homogeneous groups; the splits can be represented visually as a tree structure, meaning that models can be intuitively understood (where the tree is not too complex). Reiter [2]} used CART to generate partially synthetic microdata, as did Drechsler and Reiter [3]}, who replaced sensitive variables in the data with multiple imputations and then sampled from the multiply-imputed populations. Random forests, developed by Breiman [4]}, is an ensemble learning method and an extension to CART in that the method grows multiple trees. Random forests were used to synthesise a sample of the Ugandan Census [5]} and to generate partially synthetic microdata [6]}.
